Instructions to use stephanebhiri/lora-trained-xl-colab-slm3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use stephanebhiri/lora-trained-xl-colab-slm3 with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0", dtype=torch.bfloat16, device_map="cuda") pipe.load_lora_weights("stephanebhiri/lora-trained-xl-colab-slm3") prompt = "sks woman singer" image = pipe(prompt).images[0] - Inference
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
Download checkpoint-500/scheduler.bin from stephanebhiri/lora-trained-xl-colab-slm3: direct link, hf CLI and curl.
- Browser
- Download file 563 Bytes
-
https://huggingface.co/stephanebhiri/lora-trained-xl-colab-slm3/resolve/main/checkpoint-500/scheduler.bin
- Command line
-
hf download hf://stephanebhiri/lora-trained-xl-colab-slm3/checkpoint-500/scheduler.bin
-
curl -L -o scheduler.bin https://huggingface.co/stephanebhiri/lora-trained-xl-colab-slm3/resolve/main/checkpoint-500/scheduler.bin
563 Bytes
- Xet hash:
- 8fdff6df3c742cc2b577f37d1cb8d7902e6e9a2c557434a84786a1ebc7f4ab46
- Size of remote file:
- 563 Bytes
- SHA256:
- 62783ff6f0ae2c2ccc177b097e430c03940c5e70c38a191df273c5cf7ab1227c
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.